Premarital fertility, that is, childbearing before first marriage, is an important yet under researched demographic topic in sub-Saharan Africa. In Zimbabwe, the distinction by marital status in fertility research is hardly drawn. Hence, a gap exists in the knowledge of premarital fertility levels. This research aims to investigate levels of, and factors associated with, premarital fertility since the onset of Zimbabwe's fertility transition in the mid-1980s. The research employed direct fertility estimation techniques to effectively compare premarital, marital, and overall fertility trends between 1988 and 2015. Cox proportional-hazards regression and forest plot analyses were then used to explain changes in factors associated with the timing of premarital first births over the same period. Data quality assessments were carried out using the method of cohortperiod fertility rates to provide explanations for any erratic results. The results showed that premarital fertility was constant and moderate, with an average of 0.7 children per woman, between 1988 and 2015. While most premarital first births consistently occurred to younger women, from 2005 onwards, they increased among women aged above 24 years and decreased among adolescents. An increase in age, commencing sexual activity after adolescence, and improved socio-economic status including level of education decreased the relative risk of having a premarital first birth. However, delaying marriage past young womanhood, history of contraceptive use, Ndebele ethnicity, and residence in regions other than Manicaland and Masvingo, especially Ndebele dominated regions, increased the same risk by 465.0%, 45.5%, 136.0% and up to 135.0% respectively. The stagnation of premarital fertility between 1988 and 2015 while both marital and overall fertility first declined and then stalled indicates that there is insufficient evidence to suggest that premarital fertility had contributed to the stall of fertility decline in Zimbabwe from the mid-1990s. The timing of premarital first births since the start of the fertility transition in the 1980s has had a strong ethnic and cultural bias. Due to evidence of the effect of migrancy and tourism on premarital fertility in border and tourism towns, an extension into the theory of migrant premarital sexual behaviour to detail the risk of premarital fertility among border town residents who interact with but are neither migrants nor tourists is recommended.

Introduction/Background: Guidelines from the World Health Organization currently recommend targeting a body mass index (BMI) between 18.5 and 24.9 kg/m2 based on the lowest risk of mortality observed in epidemiological studies. However, these recommendations are based on population observations and do not take into account potential inter-individual differences. We hypothesized that genetic and non-genetic differences in adiposity, anthropometric, and metabolic measures result in inter-individual variation in the optimal BMI. Methods: Genetic variants associated with BMI as well as related adiposity, anthropometric, and metabolic phenotypes (e.g. triglyceride (TG)) were combined into polygenic risk scores (PRS), cumulative risk scores derived from the weighted contributions of each variant. 387,692 participants in the UK Biobank were split by quantiles of PRS or clinical biomarkers such as C-reactive protein (CRP), and alanine aminotransferase (ALT). The BMI linked with the lowest risk of all-cause and cause-specific mortality outcomes (“nadir value”) was then compared across quantiles (“Cox meta-regression model”). Our results were replicated using the non-linear mendelian randomization (NLMR) model to assess causality. Results: The nadir value for the BMI–all-cause mortality relationship differed across percentiles of BMI PRS, suggesting inter-individual variation in optimal BMI based on genetics (p = 0.005). There was a difference of 1.90 kg/m2 in predicted optimal BMI between individuals in the top and bottom 5th BMI PRS percentile. Individuals having above and below median TG (p = 1.29×10-4), CRP (p = 7.92 × 10-5), and ALT (p = 2.70 × 10-8) levels differed in nadir for this relationship. There was no difference in the computed nadir between the Cox meta-regression or NLMR models (p = 0.102). Conclusions: The impact of BMI on mortality is heterogenous due to individual genetic and clinical biomarker level differences. Although we cannot confirm that are results are causal, genetics and clinical biomarkers have potential use for making more tailored BMI recommendations for patients. / Thesis / Master of Science (MSc) / The World Health Organization (WHO) recommends targeting a body mass index (BMI) between 18.5 - 24.9 kg/m2 for optimal health. However, this recommendation does not take into account individual differences in genetics or biology. Our project aimed to determine whether the optimal BMI, or the BMI associated with the lowest risk of mortality, varies due to genetic or biological variation. Analyses were conducted across 387,692 individuals. We divided participants into groups according to genetic risk for obesity or clinical biomarker profile. Our results show that the optimal BMI varies according to genetic or biomarker profile. WHO recommendations do not account for this variation, as the optimal BMI can fall under the normal 18.5 - 24.9 kg/m2 or overweight 25.0 – 29.0 kg/m2 WHO BMI categories depending on individual genetic or biomarker profile. Thus, there is potential for using genetic and/or biomarker profiles to make more precise BMI recommendations for patients.

The objective of the study was to identify the factors that affect the survival lifetime of HIV+ terminal patients in rural district hospitals of Albert Luthuli municipality in the Mpumalanga province of South Africa. A cohort of HIV+ terminal patients was retrospectively followed from 2010 to 2017 until a patient died, was lost to follow-up or was still alive at the end of the observation period. Nonparametric survival analysis and semiparametric survival analysis methods were used to analyse the data. Through Cox proportional hazards regression modelling, it was found that ART adherence (poor, fair, good), Age, Follow-up mass, Baseline sodium, Baseline viral load, Follow CD4 count by Treatment (Regimen 1) interaction and Follow-up lymphocyte by TB history (yes, no) interaction had significant effects on survival lifetime of HIV+ terminal patients (p-values<0.1). Furthermore, through quantile regression modelling, it was found that short, medium and long survival times of HIV+ patients, respectively represented by the 0.1, 0.5 and 0.9 quantiles, were not necessarily significantly affected by the same factors. / Statistics / M. Sc. (Statistics)